Quality Assessment and Improvement Pathways for Machine Translation of English Technical Documents in Communication Systems
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Abstract
This study investigates quality assessment indicators and improvement strategies for machine translation (MT) of English technical documents, with a focus on communication systems and electromagnetic engineering documentation, including antenna specifications and network deployment manuals. By analyzing the limitations of traditional automated metrics such as BLEU and METEOR and integrating domain expertise, neural machine translation, and hybrid evaluation approaches, the research develops a domain-specific assessment framework. Technical optimization, professional glossary construction, corpus expansion, and standardized translation workflows are proposed to enhance translation accuracy, terminological consistency, and semantic fidelity. Experimental findings demonstrate that the integrated strategies provide more reliable evaluation results and significantly improve MT quality, thereby supporting effective technical communication and knowledge transfer in electromagnetic and communication engineering fields.
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